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High-Precision Time Delay Estimation Based on Closed-Form Offset Compensation

Yingying Li1, Hang Jiang1, Lianjie Yu1, Jianfeng Li1,2,*
1 The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
2 State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570100, China
* Corresponding Author: Jianfeng Li. Email:
(This article belongs to this Special Issue: AI-Driven Intelligent Sensor Networks: Key Enabling Theories, Architectures, Modeling, and Techniques)

Computer Modeling in Engineering & Sciences 2023, 134(3), 2123-2136. https://doi.org/10.32604/cmes.2022.021407

Received 12 January 2022; Accepted 07 April 2022; Issue published 20 September 2022

Abstract

To improve the estimation accuracy, a novel time delay estimation (TDE) method based on the closed-form offset compensation is proposed. Firstly, we use the generalized cross-correlation with phase transform (GCC-PHAT) method to obtain the initial TDE. Secondly, a signal model using normalized cross spectrum is established, and the noise subspace is extracted by eigenvalue decomposition (EVD) of covariance matrix. Using the orthogonal relation between the steering vector and the noise subspace, the first-order Taylor expansion is carried out on the steering vector reconstructed by the initial TDE. Finally, the offsets are compensated via simple least squares (LS). Compared to other state-of-the-art methods, the proposed method significantly reduces the computational complexity and achieves better estimation performance. Experiments on both simulation and real-world data verify the efficiency of the proposed approach.

Keywords

Time delay estimation (TDE); time difference of arrival (TDOA); taylor expansion; super-resolution

Cite This Article

Li, Y., Jiang, H., Yu, L., Li, J. (2023). High-Precision Time Delay Estimation Based on Closed-Form Offset Compensation. CMES-Computer Modeling in Engineering & Sciences, 134(3), 2123–2136.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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